… vehicular networks can benefit from FL. First, we discuss ML based vehicular network
applications in the context of vehicle … models in these vehicular network applications. By utilizing …

… shared machine learning model while protecting the individual data-sets. This article
investigates a new type of vehicular network concept, namely a Federated Vehicular Network (FVN)…

… [45] use FL to conduct accurate energy demand prediction with low communication overhead
for electric vehicle networks. The charging stations work as clients in FL process, and only …

… to solve many network issues in regard to data privacy and network single point of failure.
In … both federated learning and blockchain to ensure both data privacy and network security. …

… , federated learning (FL) is regarded as a promising technology to support enormous vehicular
… to improve the architecture of intelligent vehicular networks, the mobility of the vehicles …

… resources at the edge of vehicular networks. Federated learning in VEC is promising to meet
… computation capability in vehicular clients potentially affects the accuracy and efficiency of …

… vehicular platooning is optimized while jointly considering the delay of the wireless network
and the stability of the vehicle… power in a vehicular network while considering queuing …

… implemented into the vehicular network. Motivated by these, we have attempted to provide
a comprehensive survey on integrating blockchain and FL in vehicular networks in this work. …

… for intelligently supporting various vehicular services and … data sharing in vehicular edge
networks (VENs) with the … with deep Q-network and federated learning to ensure efficient …

… So this flexible learning method is suitable for autonomous driving. Vehicular edge computing
(VEC… resources at the edge of vehicular networks [10]. The edge servers such as roadside …